PRODUCE RECOGNITION METHOD

• US 20120087547A1
• Filed: 10/12/2010
• Published: 04/12/2012
• Est. Priority Date: 10/12/2010
• Status: Active Grant

• Current Assignee: NCR Voyix Corporation
• Original Assignee: NCR Corporation
• Inventors: He, Chao, Ross, Gary
• Granted Patent: US 9,412,050 B2
• Time in Patent Office: Days
• US Class Current: 382/110
• CPC Class Codes: G06F 18/253 of extracted features
• CPC Class Codes: G06V 20/68 Food, e.g. fruit or vegetables

Abstract

A produce recognition method which uses hierarchical Bayesian learning and kernel combination, and which offers classification-oriented synergistic data integration from diverse sources. An example method includes providing a classifier having a plurality of inputs, each input being adapted to receive produce data of a different modality; mapping the produce data to the respective input of the classifier by a computer; for each input, independently operating on the data relating to that input to create a feature set by the computer; comparing each feature in the feature set to respective pre-trained data for that feature to produce a similarity description set; combining all similarity description sets using a dedicated weighting function to produce a composite similarity description by the computer; and deriving a plurality of class values from the composite similarity description to create a recognition result for the produce item by the computer.

Claims

1. A method of recognizing a produce item comprising:

providing a classifier having a plurality of inputs, each input being adapted to receive produce data of a different modality;

mapping the produce data to the respective input of the classifier by a computer;

for each input, independently operating on the data relating to that input to create a feature set by the computer;

comparing each feature in the feature set to respective pre-trained data for that feature to produce a similarity description set by the computer;

combining all similarity description sets using a dedicated weighting function to produce a composite similarity description by the computer; and

deriving a plurality of class values from the composite similarity description to create a recognition result for the produce item by the computer.

2. The method according to claim 1, wherein the providing step comprises providing a Bayesian model as the classifier.

3. The method according to claim 1, wherein the providing step comprises providing a classifier with parameters derived from training involving capturing features of multiple other produce items of similar type as the one produce item.

4. The method according to claim 1, wherein the mapping step comprises utilizing kernel techniques to create the similarity description set.

5. The method according to claim 1, wherein the combining step comprises accessing an optimized weighting parameter for each of the feature similarity description sets to create the composite similarity description by the computer.

6. The method according to claim 1, wherein the deriving step comprises using regression techniques to derive the plurality of class values by the computer.

7. A method of recognizing a produce item comprising:

capturing features of multiple other produce items of similar type as the one produce item by a produce data collector;

determining parameters based upon the features of the multiple other produce items by a computer;

providing a decision engine including a Bayesian classifier configured with the parameters and having a plurality of inputs, each input being adapted to receive produce data of a different modality captured from the produce item by a produce data collector;

mapping the produce data to the respective input of the classifier by a computer;

for each input, independently operating on the data relating to that input to create a feature set by the computer;

comparing each feature in the feature set to respective pre-trained data for that feature to produce a similarity description set by the computer;

combining all similarity description sets using a dedicated weighting function to produce a composite similarity description by the computer; and

deriving a plurality of class values from the composite similarity description to create a recognition result for the produce item by the computer.

8. The method according to claim 7, further comprising

providing an indication of the identity of the produce item to an operator by the computer.

9. The method according to claim 8, further comprising

providing multiple identity predictions and probabilities and ranking them according to the probabilities for the operator by the computer.

10. A produce recognition system comprising:

a produce data collector; and

a computer for controlling the produce data collector, the computer including a decision engine including a classifier having a plurality of inputs, each input being adapted to receive produce data of a different modality;

wherein the computer is additionally for mapping the produce data to the respective input of the classifier;

for each input, independently operating on the data relating to that input to create a feature set;

comparing each feature in the feature set to respective pre-trained data for that feature to produce a similarity description set;

combining all similarity description sets using a dedicated weighting function to produce a composite similarity description; and

deriving a plurality of class values from the composite similarity description to create a recognition result for the produce item.

11. A transaction system comprising:

a produce data collector; and

a computer for controlling the produce data collector, the computer including a decision engine including a classifier having a plurality of inputs, each input being adapted to receive produce data of a different modality;

wherein the computer is additionally for mapping the produce data to the respective input of the classifier;

for each input, independently operating on the data relating to that input to create a feature set;

comparing each feature in the feature set to respective pre-trained data for that feature to produce a similarity description set;

combining all similarity description sets using a dedicated weighting function to produce a composite similarity description;

deriving a plurality of class values from the composite similarity description to create a recognition result for the produce item;

determining a price of the produce item; and

for recording payment for the produce item.

12. The transaction system according to claim 11, wherein the computer comprises a self-service computer.